Abstract
While mutation rates can vary markedly over the residues of a protein, multiple sequence alignment tools typically use the same values for their scoring-function parameters across a protein’s entire length. We present a new approach, called adaptive local realignment, that in contrast automatically adapts to the diversity of mutation rates along protein sequences. This builds upon a recent technique known as parameter advising that finds global parameter settings for aligners, to adaptively find local settings. Our approach in essence identifies local regions with low estimated accuracy, constructs a set of candidate realignments using a carefully-chosen collection of parameter settings, and replaces the region if a realignment has higher estimated accuracy. This new method of local parameter advising, when combined with prior methods for global advising, boosts alignment accuracy as much as 26% over the best default setting on hard-to-align protein benchmarks, and by 6.4% over global advising alone. Adaptive local realignment, implemented within the Opal aligner using the Facet accuracy estimator, is available at facet.cs.arizona.edu.
The work of both authors was performed at the University of Arizona.
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References
Bahr, A., Thompson, J.D., Thierry, J.C., Poch, O.: BAliBASE (Benchmark Alignment dataBASE): enhancements for repeats, transmembrane sequences and circular permutations. Nucleic Acids Res. 29(1), 323–326 (2001)
Balaji, S., Sujatha, S., Kumar, S., Srinivasan, N.: PALI—a database of Phylogeny and ALIgnment of homologous protein structures. NAR 29(1), 61–65 (2001)
Chang, J., Tommaso, P., Notredame, C.: A new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction. Mol. Biol. Evol. 31(6), 1625–1637 (2014)
DeBlasio, D., Kececioglu, J.: Facet: software for accuracy estimation of protein multiple sequence alignments (2014). facet.cs.arizona.edu
DeBlasio, D., Kececioglu, J.: Learning parameter-advising sets for multiple sequence alignment. IEEE/ACM Trans. Comput. Biol. Bioinform. (2015). doi:10.1109/TCBB.2015.2430323
DeBlasio, D.F., Wheeler, T.J., Kececioglu, J.D.: Estimating the accuracy of multiple alignments and its use in parameter advising. In: Chor, B. (ed.) RECOMB 2012. LNCS, vol. 7262, pp. 45–59. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29627-7_5
DeBlasio, D.F.: Parameter Advising for Multiple Sequence Alignment. Ph.D. dissertation, Department of Computer Science, The University of Arizona, May 2016
Do, C., Mahabhashyam, M., Brudno, M., Batzoglou, S.: Probabilistic consistency-based multiple sequence alignment. Genome Res. 15(2), 330–340 (2005)
Edgar, R.C.: BENCH (2009). drive5.com/bench
Edgar, R.: MUSCLE multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004)
Fitch, W.M., Margoliash, E.: A method for estimating the number of invariant amino acid coding positions in a gene using cytochrome c as a model case. Biochem. Genet. 1(1), 65–71 (1967)
Gotoh, O.: Optimal alignment between groups of sequences and its application to multiple sequence alignment. Comput. Appl. Biosci. 9(3), 361–370 (1993)
Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89(22), 10915–10919 (1992)
Katoh, K., Kuma, K.I., Toh, H., Miyata, T.: MAFFT version: 5 improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 33(2), 511–518 (2005)
Kececioglu, J., DeBlasio, D.: Accuracy estimation and parameter advising for protein multiple sequence alignment. J. Comput. Biol. 20(4), 259–279 (2013)
Kececioglu, J., Starrett, D.: Aligning alignments exactly. In: Proceedings of the 8th Conference on Research in Computational Molecular Biology (RECOMB), pp. 85–96. ACM (2004)
Löytynoja, A., Goldman, N.: Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science 320(5883), 1632–1635 (2008)
Müller, T., Spang, R., Vingron, M.: Estimating amino acid substitution models: a comparison of Dayhoff’s estimator, the resolvent approach and a maximum likelihood method. Mol. Biol. Evol. 19(1), 8–13 (2002)
Notredame, C., Higgins, D., Heringa, J.: T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302(1), 205–217 (2000)
Raghava, G., et al.: OXBench: a benchmark for evaluation of protein multiple sequence alignment accuracy. BMC Bioinform. 4(1), 1–23 (2003)
Roskin, K.M., Paten, B., Haussler, D.: Meta-alignment with Crumbleand Prune: partitioning very large alignment problems for performance and parallelization. BMC Bioinform. 12(1), 1–12 (2011)
Sievers, F., et al.: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Sys. Biol. 7(1), 539 (2011)
Thompson, J., Higgins, D., Gibson, T.: Clustal W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22), 4673–4680 (1994)
Van Walle, I., Lasters, I., Wyns, L.: SABmark: a benchmark for sequence alignment that covers the entire known fold space. Bioinformatics 21(7), 1267–1268 (2005)
Wheeler, T.J., Kececioglu, J.D.: Multiple alignment by aligning alignments. Bioinformatics 23(13), i559–i568 (2007)
Yang, Z.: Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Mol. Biol. Evol. 10(6), 1396–1401 (1993)
Acknowledgements
Research of JK and DD at Arizona was funded by NSF Grant IIS-1217886 to JK. DD was partially supported at Carnegie Mellon by NSF Grant CCF-1256087, NSF Grant CCF-131999, NIH Grant R01HG007104, and Gordon and Betty Moore Foundation Grant GBMF4554, to Carl Kingsford.
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DeBlasio, D., Kececioglu, J. (2017). Boosting Alignment Accuracy by Adaptive Local Realignment. In: Sahinalp, S. (eds) Research in Computational Molecular Biology. RECOMB 2017. Lecture Notes in Computer Science(), vol 10229. Springer, Cham. https://doi.org/10.1007/978-3-319-56970-3_1
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